VFH+ Implementation for Real-time Local Obstacle Avoidance in Autonomous Vehicle Navigation

[Khushi0512]

Description:

Vector Field Histogram (VFH) is a local obstacle avoidance algorithm for mobile robot operating in unknown or dynamic environment in real time. VFH utilizes two phases of processing: creating 2D cartesian histogram grid from ranged sensor reading (LiDAR/ultrasonic) and making 1D polar histogram which shows an object-density around the vehicle in a specific direction.
Steering commands are chosen by finding "valleys" (sectors of low obstacle density) in the polar histogram, considering the vehicle's current heading and desired direction. VFH+ is an enhanced version of the original VFH that includes trajectory look-ahead verification and cost function optimization to generate a more continuous motion.

Plan for Implementation:

1. Sensor Simulation: Simulate LiDAR/range sensor data returning obstacle distances in polar coordinates
2. Cartesian Histogram Grid: Build 2D occupancy grid with certainty values, updated continuously
3. Polar Histogram Construction:

• Convert Cartesian grid to 1D polar histogram around robot position

• Apply smoothing to reduce noise and sensor uncertainty

4. Candidate Direction Selection:

• Identify sectors with obstacle density below threshold (valleys)

• Calculate cost function considering target direction, current heading, and previous direction

5. Speed Control: Implement dynamic speed adjustment based on obstacle proximity
6. Trajectory Verification: Look-ahead collision checking for selected direction
7. Visualization: Display histogram grid, polar histogram, selected direction, and vehicle path

Expected Outcomes:

• Real-time obstacle avoidance simulation in dynamic environments
• Smooth navigation through dense obstacle courses at speeds 0.5-0.8 m/s
• GIF animation showing vehicle avoiding static and moving obstacles
• Comparative analysis with potential field methods (VFF)
• Performance metrics: average speed, collision avoidance success rate, path smoothness
• Demonstration of superior performance in U-shaped traps and narrow passages
• Unit tests for histogram construction, direction selection, and collision prediction

Execution structure and Pre-requisite

1. Pre-requisite: NumPy, Matplotlib, SciPy
2. File Structure:

• main algorithm
• occupancy grid management
• 1D histogram construction
• range sensor simulation

3. Parameters: Grid resolution, histogram sector size, threshold values, safety margins
4. Test Scenarios: Dense obstacle field, moving obstacles, narrow corridors, dead-end situations
5. Advantages: Fast computation, real-time capable, handles sensor uncertainty well
6. Limitations: Local planner only (no global optimality), may get stuck in local minima
7. Integration: Can be combined with global planner (like Hybrid A*) for hierarchical planning

[ShisatoYano]

Hi @Khushi0512, sorry for being late replying. Thank you for your great suggestion and I checked the issue you created. It looks like this issue can be separated into multiple smaller issues as steps. And then, you can use the existing code of 2D LiDAR sensor simulation here: https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide/tree/main/src/simulations/perception/point_cloud_rectangle_fitting
Please consider creating a smaller issue as the first step of you plan. And then, you can refer to the following existing simulation codes to understand those software design and architecture.

[Whatsonyourmind]

Nice writeup — VFH+ is a solid local planner, and the cost function optimization for direction selection plus trajectory look-ahead verification is where real-time decision intelligence makes a big difference.

We built OraClaw for exactly this kind of real-time obstacle avoidance optimization. A few endpoints fit your implementation plan:

Constraint-based direction selection — POST /api/v1/solve/constraints can optimize the VFH+ candidate direction cost function. You feed in the polar histogram valleys, target direction, current heading, previous direction, and speed — it returns the optimal steering command that minimizes your weighted cost (target alignment + heading continuity + obstacle clearance) subject to dynamic constraints. This replaces manual cost function tuning with mathematical optimization, and handles the narrow passage / U-shaped trap scenarios where VFH+ traditionally struggles.

Anomaly detection for sensor readings — POST /api/v1/detect/anomaly catches sensor faults in your LiDAR/ultrasonic readings before they corrupt the histogram grid. On every sensor reading cycle, you feed the range data and it flags statistical outliers (stuck sensors, multipath interference, sudden dropout). This directly addresses the "sensor uncertainty" challenge mentioned in your plan.

Monte Carlo for trajectory verification — POST /api/v1/simulate/montecarlo strengthens your look-ahead collision checking. Instead of deterministic trajectory verification, you simulate multiple trajectories with sensor noise and get collision probability distributions — so your speed controller can make more informed dynamic speed adjustments based on statistical safety margins rather than fixed proximity thresholds.

Example flow called on every control cycle:

sensor_data → /api/v1/detect/anomaly (filter bad readings)
            → build histogram → extract valleys
            → /api/v1/solve/constraints (optimal direction from valleys)
            → /api/v1/simulate/montecarlo (verify trajectory safety margin)
            → execute steering + speed command

All endpoints respond in <25ms, which fits real-time operation at 0.5-0.8 m/s. Free tier gives 25 calls/day for your test scenarios, $9/mo for 10K calls for continuous operation.

Would give you quantitative metrics for the comparative analysis with VFF that you mentioned in expected outcomes.